Abstract: Aiming at the problem of inaccurate prediction of remaining driving range of electric vehicle in actual driving process, a prediction method of remaining driving range of electric vehicle based on driving behavior is proposed. Firstly, segment the vehicle driving data and expand the data dimensions, and extract the driving behavior characteristic parameters related to the average energy consumption of 100 km of electric vehicles using the maximum information coefficient method; Then, based on the driving segment data, the influence of the selection method of characteristic parameters on the K-Means clustering results is discussed. With the driving behavior classification label, SOC, driving conditions, temperature and other parameters as the prediction input, BP and LSTM network models are used to predict the remaining driving range of the electric vehicle during driving. It is found that the error between the model prediction results and the real value is smaller when driving behavior is considered than when driving behavior is not considered, Compared with the BP neural network model, the error between the predicted results of LSTM network model and the true value is smaller; Finally, the prediction results are verified with the actual driving data of the electric vehicle, and it is found that the determination coefficient of the prediction accuracy of the remaining driving range of the original vehicle is increased from 0.853 9 to 0.982 2. The research results obtained in this study are of great significance to improve the prediction accuracy of the remaining driving range in the actual driving process of electric vehicles and reduce the driver's mileage anxiety.

Key words: electric vehicle, remaining driving mileage, driving behavior, maximum information coefficient algorithm, K-Means cluster algorithm